Influenza-like Illness ED Visits

Import ED Visits dataset

We will use the pared down dataset created earlier.

flu_visits = 
  read_csv("./ed_flu_tidy.csv") %>% 
  mutate(mod_zcta = as.factor(mod_zcta))

## Parsed with column specification:
## cols(
##   extract_date = col_date(format = ""),
##   date = col_date(format = ""),
##   mod_zcta = col_double(),
##   total_ed_visits = col_double(),
##   ili_pne_visits = col_double(),
##   ili_pne_admissions = col_double(),
##   pct_visits = col_double(),
##   pct_adm = col_double()
## )

Merging with our zip code-by-borough dataset will allow us to aggregate within zip codes and boroughs, and compare to other data sets with zip code data.

zip_boro = 
  read_csv("./nyc_zip_boro.csv") %>% 
  janitor::clean_names() %>% 
  mutate(zip_codes = as.factor(zip_codes))

## Parsed with column specification:
## cols(
##   Borough = col_character(),
##   `ZIP Codes` = col_double()
## )

visits_w_zip = 
  left_join(flu_visits, zip_boro, by = c("mod_zcta" = "zip_codes"))

Create a map using plotly.

visits_w_zip %>% 
  mutate(text_label = str_c(
    "Date: ", date, 
    "\n% ILI Visits: ", pct_visits,
    "\n% ILI Admissions: ", pct_adm)) %>% 
  plot_ly(
    x = ~date, y = ~total_ed_visits, color = ~borough, text = ~text_label,
    alpha = 0.3, type = "scatter", mode = "markers"
  )

## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.